



'''simple docstring'''






import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow

@require_torch
class 				a__(   unittest.TestCase      ):







    def      lowercase_			( self					:    Union[str, Any]      ):
      a		:   Tuple	  =	'ylacombe/bark-small'
      a		:   Optional[Any]	  =	tempfile.mkdtemp()
      a		:   int	  =	'en_speaker_1'
      a		:   Tuple	  =	'This is a test string'
      a		:   List[Any]	  =	'speaker_embeddings_path.json'
      a		:   Tuple	  =	'speaker_embeddings'







    def      lowercase_			( self					:    Optional[int]		,     **__snake_case					:    Optional[Any]      ):
      return AutoTokenizer.from_pretrained(self.checkpoint		,     **__snake_case      )







    def      lowercase_			( self					:    Any      ):
      shutil.rmtree(self.tmpdirname      )







    def      lowercase_			( self					:    Dict      ):
      a		:   int	  =	self.get_tokenizer()

      a		:   Dict	  =	BarkProcessor(tokenizer=__snake_case      )

      processor.save_pretrained(self.tmpdirname      )
      a		:   Any	  =	BarkProcessor.from_pretrained(self.tmpdirname      )

      self.assertEqual(processor.tokenizer.get_vocab()		,     tokenizer.get_vocab()      )







    @slow
    def      lowercase_			( self					:    List[Any]      ):
      a		:   Tuple	  =	BarkProcessor.from_pretrained(
          pretrained_processor_name_or_path=self.checkpoint		,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,     )
      processor.save_pretrained(
          self.tmpdirname		,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,     speaker_embeddings_directory=self.speaker_embeddings_directory		,     )

      a		:   Tuple	  =	self.get_tokenizer(bos_token='(BOS)'		,     eos_token='(EOS)'      )

      a		:   List[str]	  =	BarkProcessor.from_pretrained(
          self.tmpdirname		,     self.speaker_embeddings_dict_path		,     bos_token='(BOS)'		,     eos_token='(EOS)'		,     )

      self.assertEqual(processor.tokenizer.get_vocab()		,     tokenizer_add_kwargs.get_vocab()      )







    def      lowercase_			( self					:    List[str]      ):
      a		:   Optional[Any]	  =	BarkProcessor.from_pretrained(
          pretrained_processor_name_or_path=self.checkpoint		,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,     )

      a		:   str	  =	35
      a		:   Dict	  =	2
      a		:   List[Any]	  =	8

      a		:   Dict	  =	{
          'semantic_prompt': np.ones(__snake_case      ),
          'coarse_prompt': np.ones((nb_codebooks_coarse, seq_len)      ),
          'fine_prompt': np.ones((nb_codebooks_total, seq_len)      ),
      }

      # test providing already loaded voice_preset
      a		:   Optional[int]	  =	processor(text=self.input_string		,     voice_preset=__snake_case      )

      a		:   Optional[int]	  =	inputs['history_prompt']
      for key in voice_preset:
        self.assertListEqual(voice_preset[key].tolist()		,     processed_voice_preset.get(__snake_case		,     np.array([]      )      ).tolist()      )

      # test loading voice preset from npz file
      a		:   Tuple	  =	os.path.join(self.tmpdirname		,     'file.npz'      )
      np.savez(__snake_case		,     **__snake_case      )
      a		:   List[Any]	  =	processor(text=self.input_string		,     voice_preset=__snake_case      )
      a		:   int	  =	inputs['history_prompt']

      for key in voice_preset:
        self.assertListEqual(voice_preset[key].tolist()		,     processed_voice_preset.get(__snake_case		,     np.array([]      )      ).tolist()      )

      # test loading voice preset from the hub
      a		:   List[str]	  =	processor(text=self.input_string		,     voice_preset=self.voice_preset      )







    def      lowercase_			( self					:    Dict      ):
      a		:   str	  =	self.get_tokenizer()

      a		:   Optional[Any]	  =	BarkProcessor(tokenizer=__snake_case      )

      a		:   Union[str, Any]	  =	processor(text=self.input_string      )

      a		:   Dict	  =	tokenizer(
          self.input_string		,     padding='max_length'		,     max_length=2_56		,     add_special_tokens=__snake_case		,     return_attention_mask=__snake_case		,     return_token_type_ids=__snake_case		,     )

      for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]		,     encoded_processor[key].squeeze().tolist()      )




'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
     from PIL import Image

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """Salesforce/blip-image-captioning-base"""
    lowercase__					=       (
        """This is a tool that generates a description of an image. It takes an input named `image` which should be the """
        """image to caption, and returns a text that contains the description in English."""
    )
    lowercase__					=       """image_captioner"""
    lowercase__					=       AutoModelForVisionaSeq

    lowercase__					=       ["""image"""]
    lowercase__					=       ["""text"""]







    def __init__( self					:    Tuple		,     *__snake_case					:    int		,     **__snake_case					:    List[Any]      ):
      requires_backends(self		,     ['vision']      )
      super().__init__(*__snake_case		,     **__snake_case      )







    def      lowercase_			( self					:    Optional[int]		,     __snake_case					:    "Image"      ):
      return self.pre_processor(images=__snake_case		,     return_tensors='pt'      )







    def      lowercase_			( self					:    Optional[Any]		,     __snake_case					:    str      ):
      return self.model.generate(**__snake_case      )







    def      lowercase_			( self					:    Optional[Any]		,     __snake_case					:    Tuple      ):
      return self.pre_processor.batch_decode(__snake_case		,     skip_special_tokens=__snake_case      )[0].strip()